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AI Capability Maturity Model Infographic

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How to Use

  • Hover over each level to see detailed descriptions of the characteristics and capabilities at that maturity stage
  • The steps ascend from Level 1 (Ad Hoc) on the bottom to Level 5 (Transformative) at the top
  • Each level is color-coded and shows the progression from reactive to proactive AI adoption

The Five Levels:

  • Level 1 - Ad Hoc (Red): Scattered, experimental AI efforts with no formal strategy
  • Level 2 - Experimental (Orange): Small-scale pilots and growing AI awareness
  • Level 3 - Systematic (Yellow): Coordinated AI initiatives with governance frameworks
  • Level 4 - Strategic (Teal): AI integrated into core business strategy and operations
  • Level 5 - Transformative (Blue): AI fundamental to organizational identity and competitive advantage

AI Stage Terminology

  • Stage 1: Manual processes, no AI
  • Stage 2: Basic automation, simple ML
  • Stage 3: Advanced AI applications
  • Stage 4: AI-first organization
  • Stage 5: AI-native, fully integrated

This model helps organizations assess their current AI maturity and understand what capabilities they need to develop to advance to the next level. It's particularly useful for strategic planning and identifying gaps in AI readiness.

AI Maturity Model: Five Stages of Organizational AI Evolution

Stage 1: Manual Processes, No AI

"The Traditional Organization"

Characteristics:

  • All processes rely on human decision-making and manual execution
  • Data analysis done in spreadsheets or basic reporting tools
  • Knowledge workers spend significant time on repetitive tasks
  • Decision-making based on experience, intuition, and basic analytics
  • No dedicated AI/ML personnel or budget
  • Technology stack consists of traditional enterprise software (ERP, CRM, email)

Example Activities:

  • Customer service handled entirely by human agents
  • Financial forecasting done manually in Excel
  • Document processing requires human reading and data entry
  • Inventory management based on historical patterns and gut instinct
  • Hiring decisions made through traditional resume review and interviews

Pain Points:

  • High labor costs for routine tasks
  • Inconsistent decision quality across different employees
  • Slow response times to market changes
  • Limited ability to process large volumes of data
  • Difficulty scaling operations without proportional headcount increases

Stage 2: Basic Automation, Simple ML

"The Experimenting Organization"

Characteristics:

  • Introduction of robotic process automation (RPA) for routine tasks
  • Basic machine learning models for simple predictions
  • Some departments pilot AI tools independently
  • Traditional analytics supplemented with predictive models
  • IT begins exploring cloud-based AI services
  • Initial investment in data infrastructure

Example Activities:

  • Chatbots handle basic customer inquiries with human escalation
  • Simple recommendation engines for e-commerce
  • Automated invoice processing and data entry
  • Basic fraud detection using rule-based systems plus simple ML
  • Email marketing with basic personalization algorithms
  • Predictive maintenance alerts for equipment

Technologies Deployed:

  • Cloud ML services (AWS SageMaker, Google AI Platform)
  • Basic natural language processing tools
  • Simple computer vision for document scanning
  • Workflow automation tools
  • Business intelligence with predictive analytics

Challenges:

  • Siloed AI initiatives across departments
  • Limited data quality and integration
  • Lack of AI governance and standards
  • Skills gap in AI/ML expertise
  • Difficulty measuring ROI on AI investments

Stage 3: Advanced AI Applications

"The Strategic Adopter"

Characteristics:

  • Coordinated AI strategy across multiple business units
  • Advanced machine learning models in production
  • Dedicated AI team or center of excellence
  • Systematic approach to data management and governance
  • AI integrated into core business processes
  • Measurable business impact from AI initiatives

Example Activities:

  • Sophisticated demand forecasting using ensemble models
  • AI-powered personalization across all customer touchpoints
  • Computer vision for quality control in manufacturing
  • Natural language processing for contract analysis and compliance
  • Advanced analytics for supply chain optimization
  • AI-assisted decision support systems for executives

Technologies Deployed:

  • Custom machine learning pipelines
  • Real-time AI inference systems
  • Advanced analytics platforms
  • Computer vision and NLP solutions
  • AI-powered business intelligence
  • Automated feature engineering tools

Organizational Changes:

  • Chief Data Officer or Chief AI Officer roles established
  • Cross-functional AI project teams
  • Formal AI training programs for employees
  • Data science teams embedded in business units
  • AI ethics and governance committees

Capabilities:

  • Ability to build custom AI solutions
  • Integration of AI with existing enterprise systems
  • Real-time decision-making powered by AI
  • Continuous model monitoring and improvement
  • Systematic evaluation of AI project success

Stage 4: AI-First Organization

"The AI-Driven Enterprise"

Characteristics:

  • AI considerations drive major business and product decisions
  • Competitive advantage clearly attributed to AI capabilities
  • Organization-wide AI literacy and adoption
  • AI platforms enable rapid experimentation and deployment
  • Data and AI infrastructure treated as core business assets
  • AI ethics and responsible AI practices fully embedded

Example Activities:

  • Product development guided by AI-generated insights
  • Dynamic pricing algorithms that respond to market conditions in real-time
  • AI-powered workforce planning and talent acquisition
  • Autonomous customer service with minimal human intervention
  • AI-driven financial planning and risk management
  • Intelligent automation of most routine business processes

Technologies Deployed:

  • MLOps platforms for continuous model deployment
  • Federated learning systems
  • AutoML for citizen data scientists
  • Edge AI for real-time processing
  • Advanced conversational AI and virtual assistants
  • AI-powered cybersecurity and fraud prevention

Organizational Transformation:

  • AI literacy expected across all roles
  • Agile, cross-functional teams standard for AI projects
  • Continuous learning culture around AI advancement
  • Data-driven performance metrics throughout organization
  • AI impact measurement integrated into business KPIs

Strategic Advantages:

  • Faster time-to-market for new products and services
  • Superior customer experience through personalization
  • Operational efficiency gains of 20-40% in key processes
  • Predictive capabilities enable proactive business strategies
  • Ability to enter new markets enabled by AI capabilities

Stage 5: AI-Native, Fully Integrated

"The Intelligent Enterprise"

Characteristics:

  • AI is fundamental to the organization's business model and identity
  • Products and services are inherently AI-powered
  • Autonomous systems handle majority of operational decisions
  • Organization contributes to AI research and industry advancement
  • AI capabilities create entirely new revenue streams and markets
  • Continuous innovation cycle driven by AI insights

Example Activities:

  • Products that learn and improve automatically from usage
  • Fully autonomous supply chain management
  • AI-generated content and creative work at scale
  • Predictive business model pivots based on market intelligence
  • AI-powered research and development acceleration
  • Autonomous financial trading and investment decisions

Technologies Deployed:

  • Large-scale foundation models and AGI systems
  • Autonomous AI agents for complex business processes
  • AI-driven software development and testing
  • Quantum-enhanced machine learning
  • Fully integrated AI ecosystem across all business functions
  • Self-improving AI systems with minimal human oversight

Organizational Evolution:

  • Human roles focus on strategy, creativity, and AI oversight
  • Continuous adaptation enabled by AI-driven insights
  • AI democratization - all employees can leverage AI tools
  • Organization viewed as AI technology leader in industry
  • AI ethics and safety expertise is core competency

Market Position:

  • Industry leadership through AI innovation
  • Creation of new product categories enabled by AI
  • Platform business models that enable AI for others
  • Significant moats created through proprietary AI capabilities
  • Ability to rapidly enter and disrupt adjacent markets

Business Impact:

  • AI-driven revenue represents majority of total revenue
  • Competitive advantages that are difficult to replicate
  • Organizational learning and adaptation speed far exceeds competitors
  • Market valuation significantly enhanced by AI capabilities
  • Industry transformation leadership through AI innovation

Transition Indicators

From Stage 1 to 2:

  • First AI pilot projects launched
  • Cloud infrastructure investments begin
  • Initial data consolidation efforts

From Stage 2 to 3:

  • Formation of dedicated AI team
  • Multiple AI projects in production
  • Measurable business impact from AI

From Stage 3 to 4:

  • AI strategy integrated with business strategy
  • AI capabilities become competitive differentiators
  • Organization-wide AI adoption

From Stage 4 to 5:

  • AI enables new business models
  • Industry leadership in AI innovation
  • Autonomous systems handle complex decisions

This maturity model helps organizations assess their current state, understand the journey ahead, and make strategic investments aligned with their AI ambitions.

Use by AI Strategy Task Force

Lesson Plan: Using the AI Capability Maturity Model for Strategic Planning

Learning Objectives

By the end of this session, committee members will be able to:

  • Assess their organization's current AI maturity level using the CMM framework
  • Identify specific gaps and opportunities for advancement
  • Develop actionable strategies to progress to the next maturity level
  • Create alignment among stakeholders on AI strategic priorities
  • Establish measurable goals and success metrics for AI initiatives

Target Audience

AI strategy committee members, including executives, department heads, IT leaders, and key stakeholders involved in organizational AI planning and implementation.

Materials Needed

  • AI Capability Maturity Model interactive visualization
  • Printed assessment worksheets
  • Flipchart paper and markers
  • Sticky notes
  • Current organizational AI inventory (if available)

Session Structure

Opening: Framework Introduction

Activity: Interactive Exploration

  • Have each committee member individually explore the AI CMM visualization
  • Ask participants to hover over each level and read the descriptions carefully
  • Encourage note-taking on characteristics that resonate with their current organizational situation

Discussion: Understanding the Framework

  • Facilitate group discussion on the five maturity levels
  • Clarify any questions about the definitions and characteristics
  • Emphasize that this is a developmental model, not a judgment tool

Core Activity 1: Current State Assessment

Individual Assessment

  • Provide each participant with an assessment worksheet based on the CMM levels
  • Have them independently evaluate where they believe the organization currently sits
  • Encourage honest assessment across different organizational areas (data, governance, talent, infrastructure, culture)

Small Group Calibration

  • Divide into small groups of 3-4 people
  • Have groups compare individual assessments and discuss differences
  • Ask groups to reach consensus on the organization's current maturity level
  • Identify specific evidence supporting their assessment

Large Group Synthesis

  • Each small group presents their assessment and rationale
  • Facilitate discussion to build organizational consensus
  • Document areas of agreement and disagreement
  • Capture specific examples and evidence for the agreed-upon current state

Core Activity 2: Gap Analysis and Target Setting

Vision Setting

  • Have the committee discuss and agree on their target maturity level within a specific timeframe
  • Consider organizational goals, resources, and external factors
  • Document the rationale for the chosen target level

Gap Identification

  • Using the CMM descriptions, identify specific gaps between current and target states
  • Organize gaps by category (technology, people, processes, governance, culture)
  • Prioritize gaps based on impact and feasibility

Barrier Analysis

  • Brainstorm potential obstacles to advancing maturity levels
  • Categorize barriers as technical, organizational, cultural, or resource-related
  • Discuss strategies for overcoming each type of barrier

Core Activity 3: Strategic Planning

Initiative Development

  • Based on gap analysis, develop specific initiatives to advance maturity
  • Ensure initiatives address the most critical gaps identified
  • Consider dependencies between different initiatives

Resource Planning

  • Discuss resource requirements for each initiative (budget, personnel, technology)
  • Identify potential sources of funding and support
  • Consider timeline and sequencing of initiatives

Success Metrics

  • Define specific, measurable indicators of progress for each maturity level
  • Establish baseline measurements where possible
  • Create a monitoring and evaluation framework

Synthesis and Next Steps

Action Planning

  • Assign ownership for each major initiative
  • Establish immediate next steps and accountability measures
  • Schedule follow-up meetings and checkpoints

Communication Strategy

  • Develop key messages about the AI maturity assessment for different stakeholder groups
  • Plan how to communicate the strategic direction to the broader organization
  • Consider change management implications

Documentation

  • Capture all decisions, assessments, and plans in a formal document
  • Create a visual summary of current state, target state, and key initiatives
  • Establish a process for regular reassessment using the CMM framework

Assessment and Evaluation

Immediate Assessment

  • Committee members complete a brief reflection on their understanding of the organization's AI maturity
  • Collect feedback on the usefulness of the CMM framework for strategic planning

Follow-up Evaluation

  • Schedule quarterly reviews using the CMM to track progress
  • Adjust strategies based on advancement through maturity levels
  • Document lessons learned and best practices

Key Facilitation Notes

For the Facilitator:

  • Encourage honest, data-driven assessment rather than aspirational thinking
  • Help participants distinguish between current capabilities and future plans
  • Focus on specific evidence and examples rather than general impressions
  • Maintain a developmental rather than evaluative tone throughout
  • Be prepared to manage disagreements about current state assessment
  • Keep discussions focused on actionable outcomes

Common Challenges:

  • Organizations may overestimate their current maturity level
  • Different departments may be at different maturity levels
  • Some participants may focus on technology while others emphasize culture
  • Resource constraints may limit ambitious advancement plans

Extension Activities

Department-Level Assessment

  • Have individual departments complete their own CMM assessment
  • Compare departmental maturity levels to identify organizational inconsistencies
  • Develop department-specific advancement plans

Competitive Analysis

  • Research and assess competitors' apparent AI maturity levels
  • Use insights to inform strategic positioning and timing decisions

Stakeholder Engagement

  • Present CMM assessment to executive leadership and board members
  • Gather input from front-line employees on AI readiness and concerns
  • Engage with external AI experts for validation and benchmarking

Expected Outcomes

By the conclusion of this session, the committee should have:

  • A clear, consensus-based assessment of organizational AI maturity
  • Identified specific gaps and advancement opportunities
  • Developed a roadmap for progressing to the next maturity level
  • Established accountability and next steps for implementation
  • Created a framework for ongoing strategic monitoring and adjustment

This lesson plan transforms the AI Capability Maturity Model from an informational tool into a practical framework for strategic decision-making and organizational development.